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Abstract #4159

Automatic Resting State Network Decomposition using ICA and Classification in a Clinical Population

Svyatoslav Vergun 1 , Wolfgang Gaggl 2 , Veena A Nair 2 , Rasmus M Birn 3 , M. Elizabeth Meyerand 3 , James Reuss 4 , Edgar A DeYoe 5 , and Vivek Prabhakaran 2

1 Medical Physics, UW-Madison, Madison, WI, United States, 2 Radiology, UW-Madison, WI, United States, 3 Medical Physics, UW-Madison, WI, United States, 4 Prism Clinical Imaging, Inc, WI, United States, 5 Radiology, Medical College of Wisconsin, WI, United States

We present a clinically motivated, automated component decomposition and classification method using resting state functional MRI data of epilepsy and vascular/tumor patients. Preprocessed resting state scans are decomposed, with respect to their functional time series signal, using spatial independent component analysis. The resultant components are used in the classification step in which they are spatially correlated with a template compiled by a previous study. The automated classifier achieved promising performance for the visual, sensorimotor, default-mode and auditory networks.

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